Method and apparatus for estimating relative proportion of wood chips species to be fed to a process for producing pulp

ABSTRACT

Improved methods and apparatus for estimating and controlling relative proportion of wood chips originating from a plurality of sources characterized by various wood species, in a mass of wood chips to be fed to a process for producing pulp, use light reflection-related and density-related properties as input in a model characterizing a relation between such wood chip properties and species information. This principle allows efficient monitoring of the variation in wood species composition characterizing the wood chips to be processed, for the purpose of stabilizing chip feeding control and optimizing process parameters adjustment.

FIELD OF THE INVENTION

The present invention relates to the field of pulp and paper process automation, and more particularly to methods and apparatus for estimating and controlling relative proportion of wood chips originating from a plurality of sources characterized by various wood species, in a mass of wood chips to be fed to a process for producing pulp.

BACKGROUND OF THE INVENTION

Wood chips being one of the main raw materials entering into pulp production processes such as chemical (Kraft) and thermomechanical pulping (TMP) processes, variations in their physical properties have a direct impact on process control performance as well as on pulp and paper qualities. In the particular case of TMP processes, the quality of wood chips being fed to the refiners is of a great importance, since it is known to affect, along with process operating parameters, the rate of wear affecting refiner plates, as discussed by Myllyneva, J. et al. in “Fuzzy Control of Thermomechanical Pulping”, Proceedings of IMPC 1991, Minneapolis, Minn., pp. 381-384. It is well known that a typical TMP process is characterized by three critical operational variables, namely specific energy, production rate and consistency. For a given process design, specific energy consumption is the parameter that correlates most strongly to evolving pulp properties, as explained by Mosbye, K., et al. in “Use of Refining Zone Temperature Measurements for Refiner Control”, Proceedings of IMPC 2001, Helsinki, Finland, June 2001. While specific energy can in theory be kept constant through adjustments to motor load or production rate, in practice the absence of online data about dry wood chip/fibre volume and moisture content means that the control of this variable will be subjected to instability, as mentioned by Cluett, W. R., et al. in “Control and Optimization of TMP Refiners”, Pulp & Paper Canada, 96:5 (1995) pp. 31-35. The production rate, which is directly affected by the quantity of dry fibre refined, has a major impact on both energy consumption and pulp properties. The dilution water flow rate depends on chip moisture content and the consistency target as stated by Myllyneva, J. et al. in the above-mentioned reference. Consistency variations during normal operation are at least 4-6% and even higher, as reported by Hill, J., et al. in “On the Control of Chip Refining Systems”, Pulp & Paper Canada, 94:6 (1993), pp. 4347. Generally, known TMP process control strategies work according to the hypothesis that wood chip qualities are stable. Any variation in chip quality will be considered as disturbance in process control. In fact, chip quality changes quite rapidly, and known control strategies cannot efficiently eliminate its influence, which prompts fluctuations of the three operational variables of the refining process mentioned above. Wood species variation is an important factor that can negatively impact pulp quality. The lack of wood chip quality data can cause operators to conclude that the problem is caused by refiner plate wear.

A system for measuring optical reflection characteristics of chips such as brightness, along with other important chip properties, such as moisture content, which is commercially known as the Chip Management System (CMS), is described in U.S. Pat. No. 6,175,092 B1 issued to the present assignee, and in U.S published Patent application no. US 20050027482. Some pulp mills have used such system to manage their chip piles according to chip quality, as discussed by Ding et al. in “Economizing the Bleaching Agent Consumption by Controlling Wood Chip Brightness” Control System 2002 Proceedings, Jun. 3-5, 2002, Stockholm, Sweden, pp. 205-209. Chip quality assessment can be defined as the synthesis of measurements made of chip physical characteristics, as explained by Ding et al. in “Effects of Some Wood Chip Properties on Pulp Qualities” 89^(th) Pulp and Paper Annual Conference Proceedings, Jan. 29, 2003, p. 35. Ultimately, this definition depends on the importance of each chip characteristic for a given process. Sound chip pile management can help a mill to stabilize the input fed into refiners, as explained by Ding et al. in “Wood Chip Physical Quality Definition and Measurement”, 2003 International Mechanical Pulping Conference, Quebec City, Canada, Jun. 2-5, 2003, pp. 367-373.

In pulp mills, visual evaluation of wood chip quality is widely used. From the chip color, a specialist can determine the chip species and estimate freshness, bark, rot, and knot contents. A known approach consists of sorting trees according to their species or blend of species prior to wood chips manufacturing, to produce corresponding batches of wood chips presenting desired characteristics associated with these species. Typically, hardwood trees such as poplar, birch and maple are known to generally produce pale wood chips while conifers such as pine, fir and spruce are known to generally yield darker wood chips. In practice, wood chips batches can either be produced from trees of a same species or from a blend of wood chips made from trees of plural species, preferably of a common category, i.e., hardwood trees or conifers, to seek wood chips uniformity.

Many studies have shown that wood species is the dominant factor in pulping performance and pulp quality. The spruce family is the most favorable species for TMP as mentioned by Varhimo, A. et al, in “Raw Materials” in Sundbolm, J. “Mechanical Pulping” Chapter 5, Fapet O Y, 66-104 (1999). Although chip aging can be observed from chip brightness, it is only useful for substantially unvaried wood species. When an unknown proportion of wood species is present, more information is needed to provide reliable chip quality assessment. For the purpose of wood species identification, some optical testing methods are proposed by Sum, S. T. et al. in “Laser-excited Fluorescence Spectra of Eastern SPF Wood Species—An Optical Technique for Identification and Separation of Wood species”, Wood Sci. Technol., 25, 1991, pp. 405-413., and by Lawrence, A. H. in “Rapid Characterization of Wood Species by Ion Mobility Spectrometry”, Journal of Pulp and Paper Science, 15 (5), 1989, J196-J199, and a chemical vapor analysis is proposed by Fuhr, B. J. IN “On-line Wood Species Sensor”, Paper Age, September-October 2001, pp. 26-29. These known methods have been applied either off-line in laboratory or on-line for monitoring a specific wood species. However, these techniques cannot be used to evaluate a mixture involving more than two wood species. An on-line measurement system such as described in U.S published Patent application no. US 20050027482 and referred to by Ding et al. in “Effects of Some Wood Chip Properties on Pulp Qualities” 89^(th) Pulp and Paper Annual Conference Proceedings, Jan. 29, 2003, p. 35, can produce data that is useful for identifying the proportion of pure wood species making up a mixture of wood chips, on the basis of optical reflection and moisture measurements made on wood chips. For example, the brightness of Balsam Fir is quite similar to that of Black Spruce, but Fir's moisture content is about 55%, while Spruce moisture content is about 40%. Likewise, although Jack Pine's moisture content is similar to that of Black Spruce, Pine is the darker species of the two. For a mixture of more than two species, it is possible to estimate a breakdown of the species present. U.S published Patent application no. US 20050027482 teaches the use of an estimation model based on a feed-forward neural network that is built from optical reflection-based measurements, namely R,G,B,H,S,L, and dark chip content (D), along with moisture measurement as input variables, in which chip freshness (ageing) and species are controlled, and the selection of the input variables for the FFNN has been performed using known Principal Component Analysis (PCA) technique from the trials results. The well known Levenberg-Marquardt algorithm has been used to train the model, to provide at an output thereof an indication of wood species composition, usually representing the purity level of a main species forming a chip sample. However, it has been observed that such approach provides an estimation of the proportion of each species within a range of only about ±10%, which is generally insufficient to allow an efficient control over species variation in wood chips fed to the pulping process.

In a typical chemical kraft mill, the cooking process can be either batch or continuous. The wood chips are digested (cooked) at elevated temperature (about 165° C.) and pressure in “white liquor”, which is a mix of sodium sulphide (Na₂S) and sodium hydroxide (NaOH). The white liquor chemically dissolves the lignin that binds cellulose fibres together. Cooking continues until a targeted H-factor is reached. The cooking time may range from 45 to 60 minutes depending on targeted pulp grade.

As the main raw material, wood chips are the largest cost factor in the kraft pulping process. Fewer chips are currently available on the market and it is forecasted that this reduction will accelerate over the next few years. In the kraft process, chip physical characteristics such as: species, density, freshness, moisture and bark contents, and dry mass have a direct impact on pulp quality and yield. It is known to take into account off-line chip characteristics for kraft pulping process control according to strategies based on the assumption that wood characteristics are constant. Kappa number control strategies focus on H-factor control; these strategies attempt to model the relationship between kappa number, H-factor, sulphidity and effective alkali, as discussed by Hatton J. V in “Development of Yield Prediction Equations in Kraft Pulping” Tappi Journal, 56 (1973) 7, 97-100”. Usually, pulp quality control calculates H-factor sets individually for each digester to give the desired kappa number, as taught by Uusitalo P. et al in “Chemical Pulping. Papermaking Science and Technology” Book 19, Fapet Oy, Helsinki, Finland, 1999, A510p. Pulp yield, which is another important control variable related to chip characteristics, can be expressed as the ratio of pulp oven-dry weight to pulp obtained from the original wood weight. It can be measured in the mill's laboratory or estimated from the ratio of monthly wood chip tonnage to pulp tonnage. These off-line measurements are neither representative nor accurate. Online direct pulp yield measurement is very difficult as stated by Hatton J. V in the above-cited paper. For a batch cooking process, MacLeod et al proposed in “Basket Cases: Kraft Pulps Inside Digesters”, Tappi Journal 70(1987) 11, 47-53, a method that involves suspending a basket which contains a known quantity of wood chips inside the digester. This method is time consuming and requires well-trained operators and scientists, as well as ancillary equipment.

Considering the foregoing, there is still a need for improved online chip quality measurement methods and systems that can more accurately estimate the proportion of wood species present in wood chips, either in pure or mixed state, at the input of a TMP or chemical pulping process.

SUMMARY OF THE INVENTION

It is a main object of the present invention to provide improved methods and apparatus for estimating and controlling relative proportion of wood chips originating from a plurality of sources characterized by various wood species, in a mass of wood chips to be fed to a process for producing pulp, which allow efficient monitoring of the variation in wood species composition characterizing the wood chips to be processed, for the purpose of stabilizing chip feeding control and optimizing process parameter adjustment.

According to the above-mentioned main object, from a broad aspect of the present invention, there is provided a method for estimating relative proportion of wood chips originating from a plurality of sources of wood chips, in a mass of wood chips to be fed to a process for producing pulp, the wood chips of each source being characterized by one of a pure wood species and a mixture of wood species. The method comprises the steps of: i) estimating a set of wood chip properties characterizing the wood chips of said mass to generate corresponding wood chip properties data, said set including at least one light reflection-related property and at least one density-related property; and ii) feeding the wood chip properties data at corresponding inputs of a model characterizing a relation between said wood chip properties and said one of a pure species and a mixture of wood species wood chips for each source, to obtain an estimation of the wood chips relative proportion.

According to the same main object, from another broad aspect, there is provided a method for estimating relative proportion of wood chips originating from a plurality sources of wood chips, in a mass of wood chips to be fed to a process for producing pulp, the wood chips of each source being characterized by one of a pure wood species and a mixture of wood species. The method comprises the steps of: i) estimating a set of wood chip properties characterizing the wood chips of said mass to generate corresponding wood chip properties data, at least a portion of which is obtained by measuring at least one light reflection-related property and at least one density-related property; and ii) feeding the wood chip properties data at corresponding inputs of a model characterizing a relation between the wood chip properties and said one of a pure species and a mixture of wood species wood chips for each source, to obtain an estimation of the wood chips relative proportion.

According to the same main object, from a further broad aspect, there is provided an apparatus for estimating relative proportion of wood chips originating from a plurality of sources of wood chips, in a mass of wood chips to be fed to a process for producing pulp, the wood chips of each said source being characterized by one of a pure wood species and a mixture of wood species. The apparatus comprises illumination means for directing light onto an area of wood chips included in the mass of wood chips, the illuminated wood chips presenting light reflection characteristics being substantially representative of the wood chips of the mass, and an optical imaging device for sensing light reflected from the illuminated wood chips to produce image data representing at least one light reflection-related property characterizing the wood chips of the mass. The apparatus further comprises a density measuring unit for generating data representing at least one density-related property characterizing the wood chip of the mass, and a computer programmed with a model characterizing a relation between said wood chip properties and said one of a pure species and a mixture of wood species wood chips for each source, the computer processing all the data with the model to obtain an estimation of the wood chips relative proportion.

According to the same main object, from another broad aspect, there is provided a method for controlling relative proportion of wood chips originating from a plurality of sources of wood chips discharging to form a mass of wood chips to be fed to a process for producing pulp, the wood chips of each said source being characterized by one of a pure wood species and a mixture of wood species. The method comprises the steps of: i) estimating a set of wood chip properties characterizing the wood chips of said mass to generate corresponding wood chip properties data, said set including at least one light reflection-related property and at least one density-related property; ii) feeding the wood chip properties data at corresponding inputs of a model characterizing a relation between the wood chip properties and said one of a pure species and a mixture of wood species wood chips for each source, to obtain estimation data representing the wood chips relative proportion; iii) comparing the estimation data with predetermined target data to produce error data; and iv) selectively modifying the discharge rate of one or more of the wood chip sources on the basis of the error data, to adjust the relative proportion of wood chips in the mass.

According to the same main object, from a further broad aspect, there is provided a system for controlling relative proportion of wood chips originating from a plurality of sources of wood chips in communication with means for discharging thereof to form a mass of wood chips to be fed to a process for producing pulp, the wood chips of each source being characterized by one of a pure wood species and a mixture of wood species. The system comprises illumination means for directing light onto an area of wood chips included in the mass of wood chips, said illuminated wood chips presenting light reflection characteristics being substantially representative of the wood chips of said mass, and an optical imaging device for sensing light reflected from the illuminated wood chips to produce image data representing at least one light reflection-related property characterizing the wood chips of the mass. The system further comprises a density measuring unit for generating data representing at least one density-related property characterizing the wood chip of the mass and a computer programmed with a model characterizing a relation between the wood chip properties and said one of a pure species and a mixture of wood species wood chips for each said source, said computer processing all the data with said model to obtain estimation data representing the wood chips relative proportion, said computer being further programmed to compare the estimation data with predetermined target data to produce error data. The system further comprises a controller operatively connected to the discharging means for selectively modifying the discharge rate of one or more of the wood chip sources on the basis of the error data, to adjust the relative proportion of wood chips in the mass.

According to the same main object, from a further broad aspect, there is provided a software product data recording medium in which program code is stored, which program code will cause a computer to perform a method for estimating relative proportion of wood chips originating from a plurality of sources of wood chips, in a mass of wood chips to be fed to a process for producing pulp, the wood chips of each said source being characterized by one of a pure wood species and a mixture of wood species. The method comprising the steps of: i) estimating a set of wood chip properties characterizing the wood chips of the mass to generate corresponding wood chip properties data, the set including at least one light reflection-related property and at least one density-related property; and ii) feeding the wood chip properties data at corresponding inputs of a model characterizing a relation between said wood chip properties and said one of a pure species and a mixture of wood species wood chips for each said source, to obtain an estimation of the wood chips relative proportion.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings in which:

FIG. 1 is a schematic diagram of a system using a computer unit for controlling relative proportion of wood chips originating from a plurality of sources from which a mass of wood chips is formed and conveyed toward the primary refiner or digester used by the pulping process;

FIG. 2 is a partially cross-sectional end view of a main discharging screw device feeding a conveyor transporting the wood chips through the optical, moisture and volume measurement station that can be used to perform the wood species proportion estimation method of the invention;

FIG. 3 is a partially cross-sectional side view along section line 3-3 of the measurement station shown on FIG. 2 and being connected to the computer unit of FIG. 1 shown here in a detailed block diagram;

FIG. 4 is a partial cross-sectional end view along section line 4-4 of FIG. 3, showing the internal components of the measurement station;

FIG. 5 is a graph showing a set of curves representing general relations between measured optical characteristics and dark wood chips content associated with several samples;

FIG. 6 is a bar graph showing the results of online measurement of the mass of wood chips fed to the measurement station;

FIG. 7 is a PCA-X loading scatter plot of test results of chip species effect analysis;

FIG. 8 is a graph presenting the results of a validation of online moisture content measurement;

FIG. 9 shows curves representing an exemplary set of rules to be implemented in a fuzzy logic model used to perform wood species proportion estimation according to the invention;

FIG. 10 is a schematic diagram showing a neural network structure that can be used to generate the set of rules as shown in FIG. 9;

FIG. 11 is a graph showing variation of chip volume and dry mass produced by chip level control for a batch process digester wherein chip level measurement is used to control feeding volume;

FIG. 12 is a graph showing variation of chip volume and dry mass produced by chip level control for a batch process digester wherein estimated dry mass is used to control feeding volume;

FIG. 13 is a graph showing variation of wood species mixtures for different batches of wood chips fed to a digester;

FIG. 14 is a graph showing variation of density for different batches of wood chips fed to the digester;

FIG. 15 is a graph showing variation of luminance and moisture for different batches of wood chips fed to the digester;

FIG. 16 is a graph showing variation of bark content for different batches of wood chips fed to the digester;

FIG. 17 is a graph showing variables and PCA model goodness of fit and prediction for the digester;

FIG. 18 is a graph showing variable importance in the model;

FIG. 19 is a graph showing coefficients used in the model; and

FIG. 20 is a graph comparing laboratory pulp yield to the prediction of the model.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

After further weighing the importance of many chip physical characteristics, it has been found that while chip density is an important parameter closely associated with species, that variable was lacking in prior art modeling for the purpose of wood species monitoring. Basically, according to the present invention, It has been discovered that a relation between density data along with one or more light reflection-related properties in one hand, and wood species characterizing the wood chips coming from a plurality of sources in the other hand, may be implemented in a model, which can then be used to estimate relative proportion of wood chip species in a mass of wood chips obtained from said sources. It has been found that such estimation may be advantageously used for monitoring variation of wood species composition, so as to allow selective discharge adjustment of the chip sources in order to stabilize wood species composition to be fed to a TMP or chemical pulping process.

Referring now to FIG. 1, there is generally represented at 1 a system for controlling relative proportion of wood chips originating from a plurality of sources of wood chips numbered 1 to n (n=3 in the example shown), usually in the form of piles of raw wood chips 4, in communication with means for discharging such as screw devices 3, the output of which being received and transported by a main discharging screw device represented by a series of arrows 5 on FIG. 1, which screw device will be described in detail with reference to FIG. 2. The main screw discharges the wood chips as indicated by arrow 5′ to form a mass of blended wood chips 6 to be fed to a process for producing pulp, which typically makes use of a primary refiner in the case of a TMP process, or of a cooking digester in the case of a chemical process such as kraft. As will be explained below in detail, the wood chips 4 of each pile may be characterized by either a substantially pure wood species or a mixture of wood species of variable quality, depending upon available chips from providers. The system 1 includes a measurement station generally designated at 12 including an optical scanning unit 7 integrating illumination means for directing light onto a scanned area 8 of wood chips 6, and an optical imaging device for sensing light reflected from the illuminated wood chips, to produce through output line 9 image data representing at least one light reflection-related property characterizing the wood chips 6. Although only wood chips forming the top surface of the mass of wood chips 6 are illuminated and sensed, the scanning mode of operation of unit 7 ensures that these illuminated wood chips present light reflection characteristics substantially representative of all wood chips 6. The measurement station further includes a density measuring unit preferably making use of a weighing unit generally designated at 10 for measuring weight of at least a representative portion of the wood chips 6, and of a volume meter 11 for measuring volume of the same portion of wood chips. The weighing device 10 preferably makes use of a plurality of weight sensors such as load cells 40 transversely mounted in pairs along wood chip conveyer 15 and mechanically coupled to the endless belt 13 thereof to be responsive to the weight of wood chips transported by conveyer 15. The weight signals generated by load cells 40 through respective output lines 41 are combined by a weighing acquisition module 42 that produce resulting calibrated and balanced weight data. A weighing device such as Z-Block from BLH Electronics Inc. Canton, Mass., can be used. A load cell is a transducer that converts force into a measurable electrical output. Each load cell included bonded strain gauges, which are positioned so as to measure applied shear stresses. The strain gauges are wired to a Wheatstone bridge circuit which, when crossed with an excitation voltage, produces changes in the electrical output that are proportional to the applied force. Thanks to low deflection, low mass design and the absence of moving parts, such load cells afford excellent high frequency response for dynamic force measurement. Three measurements must be considered for online chip weighing, namely: wood chip weight, speed of belt 13 through line 19′ and position of main discharging screw device 17 through line 39′. A check was performed on the precision of the load cells 40. While the conveyer was running, a standard 25-kilogram weight was placed on each load cell 40. The results are shown in Table 1. TABLE 1 W_(Measurement) (kg) Test No. W_(Standard) (kg) Maximum Minimum 1 0 0 −0.2 2 25 24.9 25.1 3 50 49.8 50.2 4 75 74.9 75.1 5 100 99.7 100.2 6 125 124.7 125.5 7 150 149.2 150.0 8 175 174.5 175.2 9 200 199.8 200.2 It is to-be understood that any other suitable weighing device based on a different weight measurement principle may be used.

The volume meter 11 is preferably based on an optical ranging sensor measuring the distance separating the sensor reference plane and a scanned point 26 of the top surface of the mass of wood chips 6, from which the volume can be derived, knowing the distance separating the sensor reference plane and the surface of conveyer belt 13, and also knowing width thereof. On the conveyer, chip morphology or profile can be assumed to be constant due to the use of a proper screw spillway design, thus making it possible to infer chip volume on the basis of the bed height measurement. An infrared analog distance sensor such as model SAlD from IDEC Corporation, Sunnyvale, Calif., can be used. It is to be understood that any other suitable distance ranging device based on a different measurement principle, or any other sensor adapted to direct volume measurement, may be used. Weight and volume measurement data generated through output lines 43 and 44 respectively, are used to derive data representing at least one density-related property characterizing the mass of wood chip 6, and more specifically bulk density, as will be explained later in more detail. The system 1 further includes a computer unit 25 whose data processor is programmed with a model characterizing a relation between the wood chip properties and the wood species characteristics of the wood chips 4 of each source or pile 1 to n. The computer unit 25 is further programmed to process output data from measurement station 12 with the model to obtain estimation data representing the wood chips relative proportion. Conveniently, the data processor of computer unit 25 is used to derive the data representing density-related property data on the basis of weight and volume measurement data received from weighing device 10 and volume meter 11. The computer unit 25 is also programmed to compare the estimation data with predetermined target data to produce error data through control output line 45, which data indicate variation in the wood species composition of the wood chips to be processed. The system 1 further includes a controller unit 33 operatively connected to the drive motor (not shown) provided on each discharging screw device 3 through control lines 35 for selectively modifying the discharge rate of one or more of wood chip sources or piles 1 to n, on the basis of the error data received from computer unit 25, to adjust the relative proportion of wood chips species in the mass of wood chips 6 to be processed. The controller unit 33 is also connected to the drive motor of the main discharging screw device through further control line 35′, as will be explained below with reference to FIGS. 2 and 3. To obtain better control accuracy over the discharge adjustment, a volumetric sensor 37 is coupled to each screw device 3 to provide through feedback lines 39 a signal indicating of the effective discharge rate as a result of commands received from controller 33. A similar sensor 37′ is coupled to the main discharging screw device to provide feedback signal to controller 33 through line 39′. Conveniently, a conventional encoder mechanically or optically coupled to the driving shaft of each screw device can be used as volumetric sensor. In order to provide a more accurate estimation, the set of wood chip properties considered by the model further includes moisture content, which property is preferably measured by a moisture sensor 47 provided on the measurement station 12, producing through output line 49 data representative of the moisture content of the wood chip 6, which data is processed by computer unit 25 with the model to obtain the estimation of wood chips relative proportion on the basis of species composition. Furthermore, the moisture measurement can be also used to derive an estimation of basic density that may be advantageously used as a further input to the model, as will be later explained in more detail.

As to the weighing function of the system, the disturbance due to the fact that wood chips are falling on the conveyer belt 13 under gravity will now be defined and analysed. As shown on FIG. 2, wood chips 6 fall from a given height of typically about one meter onto belt 13 of conveyer 15. The chip's gravitational potential energy is equal to its weight times the falling distance. It is desirable to model this gravity force in order to make an assessment of a possible source of measurement error. For a given period of time, the chips fall on an area covering about 0.31×1.5 m² in the present example. Supposing that the average wood chip thickness is 5 mm, fallen chip volume is about: V=0.31×1.5×0.005=2.325×10⁻³ (m³)   (1) Assuming an average basic density p of wood chip is 450 kg/m³, the fallen chip mass is: m=ρ×V=450×2.325×10⁻³=1.04625 (kg)   (2) the chip's gravitational potential energy is: E _(C) =m×g×h=1.046253×9.81×1=10.26 (N.m)   (3) wherein:

g=acceleration of gravity=9.81 (m/s²⁾

h=chip falling height (m)

The idler reaction work is: W=F×L   (4) Wherein:

F=idler reaction force (N),

L=conveyer length (m).

According to the energy conservation law, the chip's gravitational potential energy equals the idler reaction work (E_(C)=W). Thus, by transferring values between equations (3) and (4): F=E _(C) /L=10.2637/17=0.60 N=61.18 (g)   (5) Taking into account equation (5), the chip gravity force equals idler reaction force F, and is equivalent to 61.5 (g). In practice, this force generally does not really influence measurement accuracy, as the typical analog/digital resolution of instrumentation used is about 9 (g) and its probable analog/digital system absolute error is 300 (g).

A method used by the weighing unit and computer to derive wood chips mass and density measurements will now be explained in view of the following parameters and corresponding definitions: Wet Chip Mass Modified: $m_{m} = {m_{c} + {C_{g}\frac{h_{fall}}{L}({kg})}}$ Chip Unit Length Mass: $m_{\mu} = {\frac{m_{m}}{l_{c}}\left( {{kg}\text{/}m} \right)}$ Belt Feed Forward Length: l _(f)=υ_(b) ×t (m) Chip Fall Mass: m _(d) =m _(u) ×l _(f) (kg) Chip Flow Profile: A _(s) =l _(p)×(h _(CMS) −h _(c))×C _(pc) (m²) Fall Volume: V _(d) =l _(f) ×A _(s) (m³) Fall Bulk Density: $\rho_{bulk\_ d} = {\frac{m_{d}}{V_{d}} \times {C_{bulk}\left( {{kg}\text{/}m^{3}} \right)}}$ Fall Basic Density: $\rho_{basic\_ d} = {\frac{{m_{dry}}_{- d}}{V_{d}} \times {C_{basic}\left( {{kg}\text{/}m^{3}} \right)}}$ Dry chip mass: m_(dry) _(—d) =m _(d)(1−H _(m)) Measured parameters are:

-   Belt speed: v_(b) (m/s) -   Chip Covered Length on Belt: I_(c) (m) -   Wet Chip Mass Measured: m_(c) (kg) -   Global Moisture Content: H_(m) (%) -   Height of CMS to Chip Bed: h_(c) (m)     Exemplary chip feeding configuration parameter values are: -   Chip Passage Width: I_(p)=0.31 (m) -   Height of CMS to Belt: h_(CMS)=0.18 (m) -   Chip Fall Height: h_(fall)=1 (m) -   Gravity Acceleration: g=9.81 (m/s²) -   Conveyer Length L=16.7 (m)     Coefficients and exemplary set values are: -   Chip Nominal Mass that Hits the Belt: C_(g)=0 -   Chip Flow Profile Correction Coefficient: C_(pc)=1 -   Chip Bulk Density Correction Coefficient: C_(bulk)=1 -   Chip Basic Density Correction Coefficient: C_(basic)=1     For an online chip weigh measurement, the desired outputs are chip     moisture content or weight, dry weight, bulk density and basic     density. Online chip volume data being required to calculate chip     densities, a distance sensor is used to measure chip bed height as     mentioned before. Chip dry mass and bulk and basic density can be     calculated by using the factors of chip moisture content, chip     volume and the online chip wet mass measurement. For the purpose of     experimentation, oversized and undersized chips were screened out     before entering the conveyor, thus making it possible to establish a     solid correlation between basic density and bulk density.

Assuming that load cell sampling frequency is 1/t, where t is a time interval between two samples. Belt speed is v, and the mass of chips covering the length of the conveyor is 1, a variable that will depend on the position of the chip unloading screw. For a given time, k, the chip mass falling onto the belt can be calculated as: $\begin{matrix} {{m_{d}(k)} = {\frac{m_{m}(k)}{l_{c}(k)} \times {v_{b}(k)} \times {t(k)}}} & (6) \end{matrix}$ For a given start time t₀ to end time t_(end), the total chip mass measured can be expressed as: $\begin{matrix} {{m_{total} = {\sum\limits_{k = {t\quad 0}}^{t_{end}}{m_{d}(k)}}}{{where}\text{:}}{{k = t_{0}},t_{1},{\ldots\quad t_{end}}}} & (7) \end{matrix}$ However, the wood chip mass being generally not homogeneously distributed over the belt, an error will appear in the equation (7). This error can be eliminated if the conveyer 15 is empty at the start of sampling time t₀, and the main discharging screw device 17 is stopped at end of sampling time t_(end). The measurement will be halted once and there are no longer any chips on the conveyer. As mentioned above, important variables for evaluating chip basic density and wood chip species variation are the values derived from chip wet mass and dry mass measurement. With the measurement station used in the example described above, the accuracy of load cells is better than ±0.5%. Test results are shown on FIG. 6. A validation test was performed in a TMP mill, in which, for a given volume of dry chips corresponding to 299.4 (t), the measurement station used gave a figure of approximately 290.3 (t), a result which reflects the fact that some lost, unrecoverable chips were not accounted for during the feeding stage.

The measurement station 12 is preferably based on the wood chip optical inspection apparatus known as CMS-100 chip management system commercially available from the Assignee Centre de Recherche Industrielle du Quebec (Ste-Foy, Quebec, Canada), which has the capability to measure light reflection-related properties, as well as volume and moisture content data. Such wood chip inspection apparatus is basically described in U.S. Pat. No. 6,175,092 B1 issued on Jan. 16, 2001 to the present assignee, and will be now described in more detail in the context of the estimation of wood species proportion in wood chips according to the present invention.

Referring now to FIG. 2, the measurement station 12 shown is capable of generating color image pixel data through an optical inspection technique whereby polychromatic light is directed onto an inspected area of the wood chips, followed by sensing light reflected from the inspected area to generate the color image pixel data representing values of color components within one or more color spaces (RGB, HSL) for pixels forming an image of the inspected area. The measurement station 12 comprises an enclosure 14 through which extends a powered conveyor 15 coupled to a drive motor 18. The conveyor 15 is preferably of a trough type having belt 13 defining a pair of opposed lateral extensible guards 16, 16′ of a known design, for keeping the wood chips to be inspected on the conveyor 15. In the embodiment shown on FIG. 2, only respective outlets 21 of screw devices 3 in communication with a main discharging screw device 17 are shown. It can be seen that the main discharging screw device 21 is adapted to receive through outlets 21 wood chips to be blended from corresponding wood chips sources. It is to be understood that the term “wood chips” is intended in the present specification to include other similar wooden materials for use as raw material for a particular pulp and paper process, and that could be advantageously subjected to the methods in accordance with the present invention, such as flakes, shavings, slivers, splinters and shredded wood. The main screw device 17 has an elongated cylindrical sleeve 27 of a circular cross-section adapted to receive for rotation therein a feeding screw 28 of a known construction. The sleeve 27 has lateral input openings in communication with outlet 21 allowing wood chips to reach an input portion of the screw 28. The sleeve 27 further has an output 31 generally disposed over an input end of conveyer 15 to allow substantially uniform discharge of the wood chips 6 on the conveyer belt 13. The feeding screw 28 has a base disk 30 being coupled to the driven end of a driving shaft 32 extending from a drive motor 34 mounted on a support frame (not shown), which motor 34 imparts rotation to the screw 28 at a speed (RPM) in accordance with the value of the control signal coming from controller unit 33 through line 35′, in order to modify the discharge rate of screw 28 to a desired target value. The driving control of screw devices 3 is performed in a similar way.

Turning now to FIGS. 3 and 4, internal components of the measurement station 12 and particularly of the optical scanning unit 7 as shown on FIG. 1 will be now described. The enclosure 14 is formed of a lower part 56 for containing the conveyor 15 and being rigidly secured to a base 58 with bolt assemblies 57, and an upper part 60 for containing the optical components of the station 12 and being removably disposed on supporting flanges 62 rigidly secured to upper edge of the lower part 56 with bolted profile assemblies 64. A2t the folded ends of a pair of opposed inwardly extending flanged portions 66 and 66′ of the upper part are secured through bolts 68 and 68′ side walls 70 and 70′ of a shield 72 further having top. 74, front wall 76 and rear wall 76′ to optically isolate the field of view 80 of a camera 82 for optically covering superficial wood chips 6′ that are disposed within scanned area 8 as shown in FIGS. 1 and 4, these superficial wood chips 6′ being considered as representative of the characteristics of substantially all wood chips 6. The camera 82 is located over the shield 72 and has an objective downwardly extending through an opening 84 provided on the shield top 74, as better shown on FIG. 3. Ideally, the distance separating camera objective 83 and superficial wood chips 6′ should be kept substantially constant by controlling the input flow of matter, in order to prevent scale variations that could adversely affect the optical properties measurements. However, the selective discharge adjustment that can be applied to one or more of wood chips sources 1 to n according to the wood species proportion controlling method of the invention does not generally allow a constant input flow through the measurement station 12. Therefore, the camera 82 is preferably provided with an auto-focus feature as well known in the art, and with a distance measuring feature to normalize the captured image data to compensate variation in the inspected area due to variation of the distance separating the camera reference plane and the superficial wood chips 6′ within scanned area 8 as shown in FIGS. 1 and 4. The camera 82 is used to sense light reflected on superficial wood chips 6′ to produce electrical signals representing reflection intensity values. A 2D CCD matrix, color RGB-HSL video camera such as Hitachi model no. HVC20 is used to generate the color pixel data as main optical properties considered by the method of the invention. While a 2D matrix camera is advantageously used to cover a 2D scanning area 8, it is to be understood that a suitable linear camera can alternatively be used by adapting the measurement station according to corresponding scanning parameters. Turning again to FIG. 4, diagonally disposed within shield 72 is a transparent glass sheet acting as a support for a calibrating reference support 88, whose function will be explained later in more detail. As shown on FIG. 3, the camera 82 is secured according to an appropriate vertical alignment on a central transverse member 90 supported at opposed end thereof to a pair of opposed vertical frame members 92 and 92′ secured at lower ends thereof on flanged portions 66 and 66′ as shown on FIG. 4. Also supported on the vertical frame members 92 and 92′ are front and rear transverse members 94 and 94′. Transverse members 90, 94 and 94′ are adapted to receive elongate electrical light units 96 used as illumination means, including standard fluorescent tubes 98 in the example shown, to direct light substantially evenly onto the inspected batch portion of superficial wood chips 6′. The camera 82 and light units 96 are powered via a dual output electrical power supply unit 98. Electrical image data are generated by the camera 82 through output line 7. The camera 82 is used to sense light reflected on superficial chips 6′ to generate color image pixel data representing values of color components within RGB color space, for pixels forming an image of the inspected area, which color components are preferably transformed into color components within standard LHS color space, as will be explained later in more detail. When used in cold environment, the enclosure 14 is preferably provided with a heating unit (not shown) to maintain the inner temperature at a level ensuring normal operation of the camera 82. The apparatus 10 may be also provided with air condition sensors for measuring air temperature, velocity, relative humidity, which measurement may be used to stabilize operation of the measurement station.

Referring to FIG. 3, a moisture sensor 47 is shown which is preferably part of the measurement station 12. The sensor 47 is used measure variations in the chip surface moisture content. As will be explained later in detail, the chip moisture content that can be derived from such measurement is an important property that may be advantageously considered as an input variable of the model, and that can be used to derive basic density of wood chips from bulk density measurement. The moisture sensor 47 is preferably a non-contact sensing device such as near-infrared sensor MM710 supplied by NDC Infrared Engineering, Irwindale, Calif. The sensor 47 generates at an output 79 thereof electrical signals representing mean surface moisture values for the superficial wood chips 6′.

Control and processing elements of the measurement station 12 will be now described with reference to FIG. 3. The computer unit 25 used as a data processor, which has an image acquisition module 104 coupled to line 7 for receiving color image pixel signals from camera 82, which module 104 could be any image data acquisition electronic board having capability to receive and process standard image signals such as model Meteor-2™ from Matrox Electronic Systems Ltd (Canada) or an other equivalent image data acquisition board currently available in the marketplace. The computer 25 is provided with an external communication unit 103 being coupled for bi-directional communication through lines 106 and 106′ to controller unit 33, which is a conventional programmable logic controller (PLC) programmed for controlling operation of each discharge screw device 3 through control line 35′ and feedback line 39′, as well as conveyor drive 18 through line 19 and feedback line 19′ coupled to the drive mechanism of the conveyer 15 to provide a signal indicating of the effective conveyer belt speed. The PLC 33 may receive from line 112 wood chips source data entered via an input device 114 by an operator in charge of raw wood chips management operations, such as wood chips species information. The input device 114 is connected through a further line 116 to an image processing and communication software module 118 outputting control data for PLC through line 119 while receiving acquired image data and PLC data through lines 120 and 122, respectively. The image processing and communication module 118 receives input data from a computer data input device 124, such as a computer keyboard, through an operator interface software module 126 and lines 128 and 130, while generating image output data toward a display device 132 through operator interface module 126 and lines 134 and 136. Module 118 also receives the moisture indicating electrical signals through a line 49.

Turning now to FIG. 5 general relations between measured optical characteristics and dark wood chips content associated with several samples are illustrated by the curves traced on the graph shown, whose first axis 138 represents dark chips content by weight percentage characterizing the sample, and whose second axis 140 represents corresponding optical response index measured. In the example shown, four curves 142, 144, 146, and 148 have been fitted on the basis of average optical response measurements for four (4) groups of wood chips samples prepared to respectively present four (4) distinct dark chips contents by weight percentage, namely 0% (reference group), 5%, 10% and 20%. Measurements were made using a RGB color camera coupled to an image acquisition module connected with a computer, as described before. To obtain curves 142 and 146, luminance signal values derived from the RGB signals corresponding to all considered pixels were used to derive an optical response index which is indicative of the relative optical reflection characteristic of each sample. As to curve 142, mean optical response index was obtained according to the following ratio: $\begin{matrix} {I = {\frac{L_{R}}{L_{S}} - 1}} & (8) \end{matrix}$ Wherein I is the optical response index, L_(R) is a mean luminance value associated with the reference samples and L_(S) is a mean luminance value based on all considered pixels associated with a given sample. Curve 146 was obtained through computer image processing to attenuate chip border shaded area which may not be representative of actual optical characteristics of the whole chip surface. To obtain curves 144 and 148, reflection intensity of red component of RGB signal was compared to a predetermined threshold to derive a chip darkness index according the following relation: $\begin{matrix} {D = \frac{P_{D}}{P_{T}}} & (9) \end{matrix}$ Wherein D is the chip darkness index, P_(D) is the number of pixels whose associated red component intensity is found to be lower than the predetermined threshold ratio (therefore indicating a dark pixel) and P_(T) is the total number of pixels considered. As for curve 146, curve 148 was obtained through computer image processing to attenuate chip border shaded areas. It can be seen from all curves 142, 144, 146, and 148 that the chip darkness index grows as dark chip content increases. Although curve 148 shows the best linear relationship, experience has shown that all of the above described calculation methods for the optical response index can be applied, provided reference reflection intensity data are properly determined, as will be explained later in more detail.

Returning now to FIGS. 2, 3 and 5, a preferred operation mode of the chip optical properties inspecting function of the measurement station 12 will be now explained. Referring to FIG. 3, before starting operation, the station 12 must be initialized through the operator interface module 126 by firstly setting system configuration. Camera related parameters can be then set through the image processing and communication module 118, according to the camera specifications. The initialization is completed by camera and image processing calibration through the operator interface module 126.

System configuration provides initialization of parameters such as data storage allocation, image data rates, communication between computer unit 25 and PLC 33, data file management, and wood species information. As to data storage allocation, images and related data can be selectively stored on a local memory support or any shared memory device available on a network to which the computer unit 25 is connected. Directory structure is provided for software modules and system status message file. Image rate data configuration allows to select total number of acquired images for each batch, number of images to be stored amongst the acquired images and acquisition rate, i.e. period of time between acquisition of two successive images which is typically of about 5 sec. for a conveying velocity of about 10 feet/min. Therefore, to limit computer memory requirements, while a high number of images can be acquired for statistical purposes, only a part of these images need to be stored, and most of images are deleted after a predetermined period of time. The PLC configuration relates to parameters governing communication between computer unit 25 and PLC 33, such as master-slave protocol setting (ex. DDE), memory addresses associated with <<heart beat>> for indication of system interruption, <<heart beat>> rate and wood chips presence monitoring rate. Data file management configuration relates to parameters regarding wood chips Input data, statistical data for inspected wood chips, data keeping period before deletion and data keeping checking rate. Statistical data file can typically contain information relating to source or batch number, supplier contract number, wood species identification (pure/mixture), mean intensity values for RGB signals, mean luminance L, mean H (hue) and mean S (saturation), darkness index D and date of acquisition. Data being systematically updated on a cumulative basis, the statistical data file can be either deleted or recorded as desired by the operator to allow acquisition of new data. Once the camera 82 is being configured as specified, calibration of the camera and the image processing module can be carried out by the operator through the operator interface, to ensure substantially stable light reflection intensities measurements as a function of time even with undesired lightning variation due to temperature variation and/or light source aging, and to account for spatial irregularities inherent to CCD's forming the camera sensors. Calibration procedure first consists of acquiring <<dark>> image signals while obstructing with a cap the objective of the camera 82 for the purpose of providing offset calibration (L=0), and acquiring <<lighting>> image signals with a gray target presenting uniform reflection characteristics being disposed within the inspecting area on the conveyer belt 13 for the purpose of providing spatial calibration. Calibration procedure then follows by acquiring image signals with an absolute reference color target, such as a color chart supplied by Macbeth Inc., to permanently obtain a same measured intensity for substantially identically colored wood chips, while providing appropriate RGB balance for reliable color reproduction. Initial calibration ends with acquiring image signals with a relative reference color target permanently disposed on the calibrating reference support 88, to provide an initial calibration setting which account for current optical condition under which the camera 82 is required to operate. Such initial calibration setting will be used to perform calibration update during operation, as will be later explained in more detail.

Initialization procedure being completed, the measurement station 12 is ready to operate, the computer unit 25 being in permanent communication with the PLC 33 to monitor the operation of screw drive 34 indicating discharge of wood chips blend from the sources. Whenever a new batch is detected, the following sequence of steps are performed: 1) end of PLC monitoring; 2) source or batch data file reading (species of wood chips, source or batch identification number); 3) image acquisition and processing for wood species proportion estimation; and 4) data and image recording after processing. Image acquisition consists in sensing light reflected on the superficial wood chips 6′ included in a currently inspected batch portion to generate color image pixel data representing values of color components within RGB color space for pixels forming an image of the inspected area 8 defined by camera field of view 80. Although a single batch portion of superficial chips covered by camera field of view 80 may be considered to be representative of optical characteristics of a substantially homogeneous batch, wood chips batches being known to be generally heterogeneous, it is preferable to consider a plurality of batch portions by acquiring a plurality of corresponding image frames of electrical pixel signals. In that case, image acquisition step is repeatedly performed as the superficial wood chips of batch portions are successively transported through the inspection area defined by the camera field of view 80. Calibration updating of the acquired pixel signals is performed considering pixel signals corresponding to the relative reference target as compared with the initial calibration setting, to account for any change affecting current optical condition. Superficial wood chips 6′ are also scanned by infrared beam generated by the sensor 47, which analyzes reflected radiation to generate the chip surface moisture indication signals. It is to be understood that while the moisture sensor 47 is disposed at the output of the measurement station 12 in the illustrated embodiment, other locations downstream or upstream to the measurement station 12 may be suitable.

As to image processing, the image processing and communication unit 118 is used to derive the luminance-related data, preferably by averaging luminance-related image pixel data as basically expressed as a standard function of RGB color components as follows: L=0.2125R+0.7154G+0.0721B   (10) Values of H (hue) and S (saturation) are derived from RGB data according to the same well known standard, hue being a pure color measure, and saturation indicating how much the color deviates from its pure form, whereby an unsaturated color is a shade of gray. As mentioned before, the unit 118 derives global reflection intensity data for the inspected batch portions designated before as optical response index with reference to FIG. 5, from the acquired image data. For example, experience has shown that spruce and balsam fir are brighter than jack pine and hardwood, and chip ageing and bark content decrease chip brightness. Calibration updating of the acquired pixel signals is performed considering pixels signals corresponding to the relative reference target as compared with the initial calibration setting, to account for any change affecting current optical condition. Then, image noise due to chip border shaded areas, snow and/or ice and visible belt areas are preferably filtered out of the image signals using known image processing techniques. From the signals generated by moisture sensor 47, the image processing and communication unit 118 applies compensation to the acquired pixel signals using the corresponding moisture indicating electrical signals.

Global reflection intensity data may then be derived by averaging reflection intensity values represented by either all or representative ones of the acquired pixel signals for the batch portions considered, to obtain mean reflection intensity data. Alternately, the global reflection intensity data may be derived by computing a ratio between the number of pixel signals representing reflection intensity values above a predetermined threshold value and the total number of pixel signals considered. Any other appropriate derivation method obvious to a person skilled in the art could be used to obtain the global reflection intensity data from the acquired signals. Optionally, the global reflection intensity data may include standard deviation data, obtained through well known statistical methods, variation of which may be monitored to detect any abnormal heterogeneity associated with an inspected batch.

In operation, the computer unit 25 continuously sends a normal status signal in the form of a <<heart beat>> to the PLC through line 106′. The computer unit 25 also permanently monitors system operation in order to detect any software and/or hardware based error that could arise to command inspection interruption accordingly. The image processing and communication module 118 performs system status monitoring functions such as automatic interruption conditions, communication with PLC, batch image data file management and monitoring status. These functions result in messages generation addressed to the operator through display 132 whenever appropriate action of the operator is required. For automatic interruption conditions, such a message may indicate that video (imaging) memory initialization failed, an illumination problem arose or a problem occurred with the camera 82 or the acquisition card. For PLC communication, the message may indicate a failure to establish communication with PLC 33, a faulty communication interruption, communication of a <<heart beat>> to the PLC 33, starting or interruption of the <<heart beat>>. As to batch data files management, the message may set forth that acquisition initialization failed, memory storing of image or data failed, a file transfer error occurred, monitoring of recording is being started or ended. Finally, general operation status information is given to the operator through messages indicating that the apparatus is ready to operate, acquisition has started, acquisition is in progress and image acquisition is completed.

Details regarding chip species variation analysis in relation with the present invention will now be presented in view of experimental data. As mentioned above, the measurement station is able to perform online measurement of chip physical properties, such as moisture content, darkness indication, H (Hue), S (Saturation) and L (Luminance), basic and bulk densities, dry and wet mass. Using such station, 81 tests were performed in a TMP mill over a period of nine month from spring to fall. Each test took 30 minutes, during which 10 samples of one kilogram of wood chips were taken directly from the chip feeding conveyer 15 after measurements were performed using the measurement station. The moisture content, bulk and basic density of the 10 samples were measured and averaged in the laboratory. The test results have been analyzed using Principal Component Analysis (PCA) and a PCA-X loading scatter plot of these results are presented in FIG. 7, in which SF stands for a Spruce and Balsam Fir mixture, HW for a Hardwood mixture and PH for a Pine and Hemlock mixture. The percentage of each species in the mixtures of SF, HW and PH is unknown, but can be assumed to be stable. As shown on FIG. 7, for the first component p[1], bulk density is directly proportional to L, moisture content and SF, while basic density is directly proportional to H, S, darkness and PH. For the second component p[2], the SF value is significantly inversely proportional to the HW but only slightly so in the case of PH and bulk and basic densities. Therefore, it would appear that wood species data is a critical factor in online measurements performed.

Referring now to FIG. 8, the results of a validation of online moisture content measurement is presented, in which the wood species is a mixture of SF, PH and HW, and their respective proportions are unknown. The test period extended over six month from spring to fall, and measurement accuracy was estimated within about ±1%.

Details concerning wood species modelling in relation with the present invention will now be presented. Based on the PCA analysis results, a fuzzy logic model has been built in order to estimate the SF, HW and PH proportions in a mixture. Fuzzy logic is a structured, model-free estimator that approximates a function through linguistic input/output associations. As mentioned in the previous section, there are preferably 7 inputs (H, S, L, Moisture Content, Darkness, Bulk and Basic Density) and 3 outputs (SF %, PH % and HW %). Wood species estimation model preferably includes n fuzzy logic models (3 in the presented example), i.e., one model associated with each wood chip source, for estimating the percentage of the wood chips coming therefrom, either characterized by pure of mixed wood species as mentioned before. The sum of the outputs of the fuzzy Logic model equals 100%. The modelling procedure, that can be performed using any suitable commercially available fuzzy logic software tool such as provided by Mathlab™, typically involves the following steps:

-   -   1. Fuzzifying inputs: determining the degree to which they         belong to each of the appropriate fuzzy sets (the inputs) via         the membership functions relating to Gaussian distribution         curves;     -   2. Inference: using the Takagi-Sugeno-Kang method as an         inference motor and a combination of back propagation and least         squares in order to gather data from the mill's test results and         thus be able to compute the membership function parameters.         These parameters are best suited to enable the associated fuzzy         inference motor to track the given input and output data.     -   Defuzzification method: weighted average.

As an example, a fuzzy logic model having 4 rules defined by the graphs associated with each of the 7 inputs as shown on FIG. 9, which rules are generated using a neural network structure as shown on FIG. 10, can be used. Using such fuzzy logic model, 15 tests were performed in a TMP mill. The prediction results obtained compared with laboratory trials are shown in Table 2. TABLE 2 SF % PH % HW % No Date Lab. Model Lab. Model Lab. Model 1 22/03/04 80 81 5 4 15 15 2 26/03/04 80 74 5 9 15 17 3 02/04/04 85 79 5 6 15 15 4 20/04/04 85 80 5 7 15 15 5 05/05/04 70 51 10 20 20 29 6 09/06/04 70 80 10 8 20 12 7 05/10/04 85 80 8 11 7 9 8 08/10/04 85 79 8 11 7 10 9 12/10/04 85 77 8 12 7 11 10 15/10/04 85 78 8 11 7 11 11 20/10/04 70 76 20 12 10 12 12 25/10/04 70 71 20 15 10 14 13 28/10/04 65 69 15 15 20 16 14 08/11/04 65 74 15 12 20 14 15 11/11/04 65 69 15 13 20 18 The chip samples are mixtures of SF, PH and HW. The percentage of pure species in each mixture of SF, PH and HW is unknown, but can be considered to be stable. As shown in the table, model prediction accuracy is very good (±5-10%). If the SF, PH and HW chips are pure species, prediction accuracy can be increased further.

Similar results were obtained using a model based on Projections to Latent Structures (PLS) Assuming P(i)=f(H, S, L, Darkness, Moisture Content, Bulk Density, Basic Density), with i=1, 2, 3 . . . n being the number of chip sources, each containing either pure species or a mixture of species, P being the proportion of chips (from a chip pile) in the mixture, we have: $\begin{matrix} {{\sum\limits_{i = 1}^{n}{P(i)}} = {100\%}} & (11) \\ {{P(i)} = {{a_{i}H} + {a_{2}S} + {a_{3}L} + {a_{4}D} + {a_{5}M} + {a_{6}\rho_{bulk}} + {a_{7}\rho_{Basic}} + C}} & (12) \end{matrix}$ wherein a₁-a₇ are coefficients and C is a constant.

In operation, based on the principle of the present invention, online measurements can be combined with control of the speed of the chip feeding screw 3 for each pile to produce stable values for chip species before chips enter the refiner or digester. The invention can also help operators to better control plate gap, dilution water rate in view of production rate, specific energy and consistency control, and also can serve to warn operators whenever unacceptable chips are likely to enter the process and negatively impact pulp quality.

Some considerations more specific to the application of the present invention to a chemical pulp process will now be discussed. For a given batch digester used in the kraft pulping process, the physical characteristics of wood chips vary broadly from batch to batch. One of the objectives of batch digester control is to achieve maximum pulp production at a predetermined degree of pulp delignification such as chemically measured by the permanganate number (P number) with minimum chemicals input. For a given batch digester used in the kraft pulping process, the physical characteristics of wood chips vary broadly from batch to batch. For batch digester cooking control, the monitoring method of the present invention preferably makes use of an online chip characteristics measurement system as described above. Based on online measurement information related to specific parameters, chip feeding and alkali filling can be stabilized. More particularly, a general pulp yield prediction model (PLS) developed to optimize the kraft process and maximize pulp yield can be used.

As mentioned above, the measurement system provides online information on chip brightness, bark content, chip dynamic weight, moisture, chip wet and dry mass flow rate, basic and bulk density, volume flow rate and proportions of wood chips from the different piles. When installed in the chip feeding process, the measurement system generates online chip characteristics information that can be used to control the mixture of chips from the different piles in order to stabilize the dry mass of wood chips entering the digester. Online chip information can also help the operator to control liquor filling for a batch digester; and production rate, alkali-to-wood ratio dosage, etc. for a continuous digester.

Generally, batch digester controls involves many parameters, including production rate control, cooking cycle controls (chip feeding, liquor filling, steam filling, heating and cooking, blowing), scheduling, steam levelling and quality control, as well known in the art, which are explained in detail by Leiviskä K. in “Process Control Papermaking Science and Technology” Book 14, Fapet Oy, Jyväskylä, Finland, 1999, 82 p. The effect of selected online chip characteristics on chip feeding and liquor filling control in order to increase pulp yield will now be discussed in view of examples based on experimental works performed in a typical batch kraft mill where chip feeding control was based either on chip level measurement in the batch digester or on estimated dry mass.

As the inner volume of the digester was constant, the chip feeding volume was calculated from the chip level measurement. In the mill, three chip piles were classified as low, medium and high density. Pile 1, the low-density chip pile, was a mixture of two wood species; Pile 2, the medium-density chip pile, was a mixture of two other wood species; and Pile 3, the high-density pile, consisted of another 3 or 4 wood species. A fixed percentage of wood chips from the three piles was fed into the digester. Assuming that the moisture content, bark content, chip size distribution, bulk density and wood species in the mixture were arbitrary constant, the chip dry mass was calculated from the chip level measurement. Referring to FIG. 11, the system measurements show large variations in wood chip volumes being fed into the digester on the basis of chip level control. Excluding volumes of less than 70 m³, which are abnormal, the average volume is about 99.69 m³ with a standard deviation of 19.19. This error cannot be overcome when chip level measurement is used to control feeding volume, as chip size distribution, compacting, etc. strongly influence chip fill-in volume. For this reason, dry mass control is preferably used instead of chip level to control digester fill-in. As illustrated on FIG. 12, the measurement system provided accurate chip dry mass measurement, the absolute error being about 0.2 kg in the range of [0, 200] kg with a moisture content measurement accuracy of about ±1.0%. With this online sensor and the same digester, chip dry mass was maintained around 16,000 kg. According to experience and laboratory test results, an optimum liquor-to-wood ratio was defined, allowing the operator to control liquor dosage.

Pulp yield is a major factor for a chemical pulp mill. It can be expressed as the ratio of pulp oven-dry weight to pulp obtained from the original wood weight. However, prior to the present invention, no online pulp yield measurement system was available to assess pulping process efficiency. The yield from kraft pulping varies with wood types, the extent of lignin removal and cooking conditions. Hatton in his above cited paper proposed a very neat yield prediction equation: Y=A−B(log H)(EA)^(n)   (13) where Y=total pulp yield (%);

-   -   H=H-factor, a pulping variable that combines cooking temperature         and time into a single variable indicating the extent of the         reaction;     -   EA=effective alkali charge, i.e. ingredients that will actually         produce alkali under pulping conditions; and     -   A, B, n=species-dependent constants.         However, this equation is not applicable to kraft mills for two         main reasons. First wood species, moisture content, density,         etc. vary with time and from batch to batch. If these variations         cannot be measured online, the constants A, B and n should not         be identified online. Second, equation (13) was based on a         laboratory test with four pure wood species: western hemlock,         western red cedar, jack pine and trembling aspen. In practice,         the use of other wood species or a variable species mixture in a         mill, and even variations in density, age, etc. in a stable wood         species also affect the precision of model predictions.         Therefore, that model is very difficult to apply in an actual         mill environment, and no known prior art method or system can         accurately predict pulp yield in such case.

Chip density and wood species affect kraft pulp yield. High-density and hardwood chips lead to higher pulp yield while low-density and softwood chips lead to lower yield due to higher initial lignin content. For a given liquor-to-wood ratio, i.e. total liquor in the batch digester to amount of dry chips, the quantity of dry chips is an indicator of alkali dosage, but moisture content variations lead to variations in alkali dosage. Variations in the wood species mixture (proportion of wood chips from three piles), volume, wet mass and moisture content affect not only the dosage of alkali to wood, but also pulp yield and pulp quality. Chip brightness (luminance) is an important indicator of chip decay; older chips contain more decayed chips. Decayed chips contain a high lignin content requiring more alkali to be dissolved. The lignin content of bark is generally much higher than that of wood chips for the same wood species; as with decayed chips, a higher bark content requires much more alkali to be dissolved. Both decayed chips and bark also have a negative impact on pulp quality.

Online measurements of relevant chip characteristics solve this problem, allowing the control of chip pile dosage screw speed in order to reduce wood species fluctuation, as well as the control of digester chip feeding in order to maintain chip dry mass from batch to batch. Furthermore, it allows the control of alkali filling according to the chip characteristics fed into the digester. The variations in chip characteristics measured by the system for different batches in a given digester are plotted in the graphs of FIGS. 13, 14, 15 and 16, respectively representing variations of wood species mixtures, density (bulk, basic), luminance-moisture and bark content.

Since online information on variations in wood chip characteristics cannot be used in Equation (13), a new pulp yield model has been developed, which is applicable to any batch digester cooking process irrespective of the wood chips used. For so doing, a test was performed in a kraft mill wherein 142 process observations were recorded relating to the batch digester cooking parameters listed in Table 3. TABLE 3 Wood Chips Species Bulk Moisture Luminance Dry Density Content Mass Volume Basic Bark Content H, S, L Wet Density Mass Cooking % sulfity Liquor Cooking EA on O.D. wood H factor Temperature cycle Control Control Permanganate Residual Pulp yield Parameters number alkali Taking into account equation (13) and using a Principle Component Analysis (PCA) to assess the contribution of each variable to the model, we chose 15 parameters were chosen to describe the kraft process in the batch digester, as shown in FIG. 17. The goodness of fit of the model was R²X=0.975 (explained variation) and the goodness of prediction of the model was Q²=0.651 (predicted variation). On the basis of wood chip online measurement information and PCA model results, a model based on Projections to Latent Structures (PLS) was developed to predict pulp yield, variable importance and coefficients of which model being graphically shown in FIGS. 18 and 19, respectively. The model can be expressed as: $\begin{matrix} {{PulpYield} = {{\sum\limits_{i = 1}^{m}\quad{k_{i}V_{i}}} + C}} & (14) \end{matrix}$ where V_(i)=value of parameter i;

-   -   k_(i)=model coefficients for parameter i;     -   C=constant     -   m=variable number (14 in the example shown).         The most important variables for the model are: effective         alkali, chip wet mass, hue and moisture content. The         stabilization of wood chip wet mass, moisture content and         effective alkali increases and stabilizes pulp yield for a given         wood chip mixture. Referring to FIG. 20 showing a graph         comparing laboratory pulp yield to the prediction of the model         of Equation (14), a good correlation is observed, with a         coefficient about 0.99. Following experimental trials under         actual mill conditions wherein equation (14) and related control         were used, a pulp yield increase of 2% (e.g., from 48% to 50%)         was obtained for a same productivity, which results in daily         savings of 12.5 metric tonnes of wood chips, to which one may         add savings in white liquor and improved pulp quality.

Although the preferred embodiments of the present invention was described above in detail with respect to typical TMP and kraft batch process, it is to be understood that the estimation methods and system of the invention may be used in continuous pulping process by providing appropriate adaptation to take into account dynamic parameters such as flow rates and delays. 

1. A method for estimating relative proportion of wood chips originating from a plurality of sources of wood chips, in a mass of wood chips to be fed to a process for producing pulp, said wood chips of each said source being characterized by one of a pure wood species and a mixture of wood species, said method comprising the steps of: i) estimating a set of wood chip properties characterizing the wood chips of said mass to generate corresponding wood chip properties data, said set including at least one light reflection-related property and at least one density-related property; and ii) feeding said wood chip properties data at corresponding inputs of a model characterizing a relation between said wood chip properties and said one of a pure species and a mixture of wood species wood chips for each said source, to obtain an estimation of said wood chips relative proportion.
 2. The method of claim 1, wherein said at least one density-related property includes one of basic density and bulk density of the wood chips of said mass.
 3. The method of claim 1, wherein said at least one density-related property includes basic density and bulk density of the wood chips of said mass.
 4. The method of claim 1, wherein said set of wood chip properties further includes moisture content.
 5. The method of claim 1, wherein said at least one light reflection-related wood chip property data is expressed as at least one optical parameter representing light reflection characteristics of the wood chips of said mass.
 6. The method of claim 5, wherein said optical parameter is luminance.
 7. The method of claim 5, wherein said optical parameter is selected from the group consisting of hue, saturation, luminance and darkness indicator.
 8. The method of claim 7, wherein said set of wood chip properties further includes moisture content.
 9. The method of claim 1, wherein said at least one light reflection-related wood chip property data is expressed as a plurality of optical parameters representing light reflection characteristics of the wood chips of said mass, including hue, saturation and luminance.
 10. The method of claim 9, wherein said plurality of optical parameters further include darkness indicator.
 11. The method of claim 1, wherein said set of wood chip properties further includes moisture content.
 12. A method for estimating relative proportion of wood chips originating from a plurality sources of wood chips, in a mass of wood chips to be fed to a process for producing pulp, said wood chips of each said source being characterized by one of a pure wood species and a mixture of wood species, said method comprising the steps of: i) estimating a set of wood chip properties characterizing the wood chips of said mass to generate corresponding wood chip properties data, at least a portion of which is obtained by measuring at least one light reflection-related property and at least one density-related property; and ii) feeding said wood chip properties data at corresponding inputs of a model characterizing a relation between said wood chip properties and said one of a pure species and a mixture of wood species wood chips for each said source, to obtain an estimation of said wood chips relative proportion.
 13. The method of claim 12, wherein said at least one density-related property includes one of basic density and bulk density of the wood chips of said mass.
 14. The method of claim 13, wherein said step i) includes the steps of: a) measuring weight of the wood chips of said mass; b) measuring volume of the wood chips of said mass, c) deriving bulk density data from said measured weight and volume of the wood chips of said mass.
 15. The method of claim 14, wherein said step i) further include the steps of: d) measuring moisture content of the wood chips of said mass; e) deriving basic density data from said measured weight, volume and moisture content of the wood chips of said mass.
 16. The method of claim 12, wherein said set of wood chip properties further includes moisture content as estimated by said measured moisture content of the wood chips of said mass.
 17. The method of claim 1, wherein said at least one light reflection-related wood chip property data is expressed as at least one optical parameter representing light reflection characteristics of the wood chips of said mass.
 18. The method of claim 17, wherein said optical parameter is luminance.
 19. The method of claim 17, wherein said optical parameter is selected from the group consisting of hue, saturation, luminance and darkness indicator.
 20. The method of claim 19, wherein said set of wood chip properties further includes moisture content.
 21. The method of claim 12, wherein said at least one light reflection-related wood chip property data is expressed as a plurality of optical parameters representing light reflection characteristics of the wood chips of said mass, including hue, saturation and luminance.
 22. The method of claim 21, wherein said plurality of optical parameters further include darkness indicator.
 23. The method of claim 12, wherein said set of wood chip properties further includes moisture content.
 24. An apparatus for estimating relative proportion of wood chips originating from a plurality of sources of wood chips, in a mass of wood chips to be fed to a process for producing pulp, said wood chips of each said source being characterized by one of a pure wood species and a mixture of wood species, said apparatus comprising: illumination means for directing light onto an area of wood chips included in said mass of wood chips, said illuminated wood chips presenting light reflection characteristics being substantially representative of the wood chips of said mass; an optical imaging device for sensing light reflected from the illuminated wood chips to produce image data representing at least one light reflection-related property characterizing the wood chips of said mass; a density measuring unit for generating data representing at least one density-related property characterizing the wood chip of said mass; and a computer programmed with a model characterizing a relation between said wood chip properties and said one of a pure species and a mixture of wood species wood chips for each said source, said computer processing all said data with said model to obtain an estimation of said wood chips relative proportion.
 25. The apparatus of claim 24, wherein said at least one density-related property density includes bulk density, said density measuring unit including: a weighing device for measuring weight of at least a representative portion of the wood chips of said mass; a volume meter for measuring volume of said representative portion of the wood chips of said mass; a data processor for deriving bulk density data from said measured weight and volume of the wood chips of said mass.
 26. The apparatus of claim 25, wherein said data processor is included in said computer.
 27. The apparatus of claim 24, wherein said at least one density-related property density includes basic density, said apparatus further comprising: a moisture sensor for measuring moisture content of the wood chip of said mass; said density measuring unit including: a weighing device for measuring weight of at least a representative portion of the wood chips of said mass; a volume meter for measuring volume of said representative portion of the wood chips of said mass; a data processor for deriving basic density data from said measured weight, volume and moisture content of the wood chips of said mass.
 28. The apparatus of claim 27, wherein said data processor is included in said computer.
 29. The apparatus of claim 24, wherein said set of wood chip properties further includes moisture content, said apparatus further comprising: a moisture sensor for producing data representative of the moisture content of the wood chip of said mass, which data being processed by said computer with said model to obtain the estimation of said wood chips relative proportion.
 30. A method for controlling relative proportion of wood chips originating from a plurality of sources of wood chips discharging to form a mass of wood chips to be fed to a process for producing pulp, said wood chips of each said source being characterized by one of a pure wood species and a mixture of wood species, said method comprising the steps of: i) estimating a set of wood chip properties characterizing the wood chips of said mass to generate corresponding wood chip properties data, said set including at least one light reflection-related property and at least one density-related property; ii) feeding said wood chip properties data at corresponding inputs of a model characterizing a relation between said wood chip properties and said one of a pure species and a mixture of wood species wood chips for each said source, to obtain estimation data representing said wood chips relative proportion; iii) comparing said estimation data with predetermined target data to produce error data; and iv) selectively modifying the discharge rate of one or more of said wood chip sources on the basis of the error data, to adjust the relative proportion of wood chips in said mass.
 31. A system for controlling relative proportion of wood chips originating from a plurality of sources of wood chips in communication with means for discharging thereof to form a mass of wood chips to be fed to a process for producing pulp, said wood chips of each said source being characterized by one of a pure wood species and a mixture of wood species, said system comprising: illumination means for directing light onto an area of wood chips included in said mass of wood chips, said illuminated wood chips presenting light reflection characteristics being substantially representative of the wood chips of said mass; an optical imaging device for sensing light reflected from the illuminated wood chips to produce image data representing at least one light reflection-related property characterizing the wood chips of said mass; a density measuring unit for generating data representing at least one density-related property characterizing the wood chip of said mass; a computer programmed with a model characterizing a relation between said wood chip properties and said one of a pure species and a mixture of wood species wood chips for each said source, said computer processing all said data with said model to obtain estimation data representing said wood chips relative proportion, said computer being further programmed to compare said estimation data with predetermined target data to produce error data; and a controller operatively connected to said discharging means for selectively modifying the discharge rate of one or more of said wood chip sources on the basis of the error data, to adjust the relative proportion of wood chips in said mass.
 32. The system of claim 31, wherein said at least one density-related property density includes bulk density, said density measuring unit including: a weighing device for measuring weight of at least a representative portion of the wood chips of said mass; a volume meter for measuring volume of said representative portion of the wood chips of said mass; a data processor for deriving bulk density data from said measured weight and volume of the wood chips of said mass.
 33. The system of claim 32, wherein said data processor is included in said computer.
 34. The system of claim 31, wherein said at least one density-related property density includes basic density, said system further comprising: a moisture sensor for measuring moisture content of the wood chip of said mass; said density measuring unit including: a weighing device for measuring weight of at least a representative portion of the wood chips of said mass; a volume meter for measuring volume of said representative portion of the wood chips of said mass; and a data processor for deriving basic density data from said measured weight, volume and moisture content of the wood chips of said mass.
 35. The system of claim 34, wherein said data processor is included in said computer.
 36. The system of claim 31, wherein said set of wood chip properties further includes moisture content, said system further comprising: a moisture sensor for producing data representative of the moisture content of the wood chip of said mass, which data being processed by said computer with said model to obtain the estimation of said wood chips relative proportion.
 37. A software product data recording medium in which program code is stored, which program code will cause a computer to perform a method for estimating relative proportion of wood chips originating from a plurality of sources of wood chips, in a mass of wood chips to be fed to a process for producing pulp, said wood chips of each said source being characterized by one of a pure wood species and a mixture of wood species, said method comprising the steps of: i) estimating a set of wood chip properties characterizing the wood chips of said mass to generate corresponding wood chip properties data, said set including at least one light reflection-related property and at least one density-related property; and ii) feeding said wood chip properties data at corresponding inputs of a model characterizing a relation between said wood chip properties and said one of a pure species and a mixture of wood species wood chips for each said source, to obtain an estimation of said wood chips relative proportion. 